Kimberly Shenk, Novi’s CEO, shares how decades in data science, from military missions to tech startups, taught her that the real power behind AI and LLMs isn’t the model, it’s the data you can trust.
Generative Engine Optimization
Pranav Aggarwal et al., Princeton University (2024)
This paper introduces the concept of Generative Engine Optimization (GEO), a framework for improving how online content appears in AI-generated answers from models like ChatGPT and Gemini. Through large-scale experiments, the authors show that verified, well-structured information increases visibility by up to 40%, proving that credibility now drives discoverability in AI systems.
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Role-Augmented Intent-Driven Generative Search Engine Optimization (G-SEO)
Xiaolu Chen et al., Shanghai Jiao Tong University (2025)
Building on GEO, this research explores how AI models interpret user intent and role-based context when generating responses. The paper presents a system called G-SEO that adapts content for generative search by analyzing how large language models prioritize structured and trustworthy data.
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Beyond Keywords: Driving Generative Search Engine Optimization With Content-Centric Agents
Qiyuan Chen et al., Tsinghua University (2025)
Qiyuan Chen et al., Tsinghua University (2025)
This paper expands on the field of Generative Search Engine Optimization (GSEO) by introducing a multi-agent system that helps content creators adapt information for generative models. The authors present CC-GSEO-Bench, a large-scale benchmark for evaluating how content quality, structure, and credibility affect AI-generated visibility. Their findings reinforce that AI systems consistently favor verified, well-structured information.
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This guide breaks down how AI chooses products, why verified data matters more than ever, and how brands can make sure their holiday best sellers actually show up when it counts.
Stop optimizing for static moments and start influencing contextual conversations.
